Data-driven monitoring of the gearbox using multifractal analysis and machine learning methods

Abstract

Data-driven diagnostic methods allow to obtain a statistical model of time series and to identify deviations of recorded data from the pattern of the monitored system. Statistical analysis of time series of mechanical vibrations creates a new quality in the monitoring of rotating machines. Most real vibration signals exhibit nonlinear properties well described by scaling exponents. Multifractal analysis, which relies mainly on assessing local singularity exponents, has become a popular tool for statistical analysis of empirical data. There are many methods to study time series in terms of their fractality. Comparing computational complexity, a wavelet leaders algorithm was chosen. Using Wavelet Leaders Multifractal Formalism, multifractal parameters were estimated, taking them as diagnostic features in the pattern recognition procedure, using machine learning methods. The classification was performed using neural network, k-nearest neighbours’ algorithm and support vector machine. The article presents the results of vibration acceleration tests in a demonstration transmission system that allows simulations of assembly errors and teeth wear.

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

“…It can be seen that its shape differs from the parabolic one, which is typical for multifractal objects. Similar forms of multifractal spectrum are presented in the works of other authors [37][38][39]. According to the graph of the generalized Hurst exponent (Figure 9), it can be seen that the acoustic signals of a defective pipeline are characterized by a pronounced nonlinear dependence h(q).…”

“…It can be seen that its shape differs from the parabolic one, which is typical for multifractal objects. Similar forms of multifractal spectrum are presented in the works of other authors [37][38][39]. With the appearance of a leak in the pipeline, the width of the multifractal spectrum increases, they acquire a parabolic shape and shift along the axis of the abscissa (Figure 12).…”

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

“…Compared to previous papers [48], the experiment was extended with addit termediate operating states and the analysis method was modified. Vibration acceleration signals were recorded for a rotational speed of about 1 and a load of 12%-pressure 0.6 MPa.…”

“…The signals presented in the graph represent a si degree of damage, and their characteristic features are visible. Compared to previous papers [48], the experiment was extended with additional intermediate operating states and the analysis method was modified.…”

Diagnosis of sensor faults in a combustion engine control system with the artificial neural network

“…Artificial Neural Networks (ANN) can work easily with non-linearly separable data, and this makes them ideal for applications such as fault detection and classification, where the training data are sparse, and the network will have to generalize well. Several applications have demonstrated that a neural network can successfully recognize and classify different faults in a number of different condition monitoring applications [16][17][18][19][20][21][22]. A good general introduction to neural networks is provided by [23,24].…”